Amino acids are among the building blocks of life, forming peptides and proteins, and have been carefully ‘selected’ to prevent harmful reactions caused by light. To prevent photodamage, molecules relax from electronic excited states to the ground state faster than the harmful reactions can occur; however, such photochemistry is not fully understood, in part because theoretical simulations of such systems are extremely expensive—with only smaller chromophores accessible. Here, we study the excited-state dynamics of tyrosine using a method based on deep neural networks that leverages the physics underlying quantum chemical data and combines different levels of theory. We reveal unconventional and dynamically controlled ‘roaming’ dynamics in excited tyrosine that are beyond chemical intuition and compete with other ultrafast deactivation mechanisms. Our findings suggest that the roaming atoms are radicals that can lead to photodamage, offering a new perspective on the photostability and photodamage of biological systems.

氨基酸是生命的基石之一，形成肽和蛋白质，并经过精心“选择”以防止由光引起的有害反应。为了防止光损伤，分子从电子激发态弛豫到基态的速度快于有害反应发生的速度；然而，这种光化学还没有被完全理解，部分原因是这种系统的理论模拟非常昂贵——只能获得较小的发色团。在这里，我们使用基于深度神经网络的方法研究酪氨酸的激发态动力学，该方法利用量子化学数据背后的物理学并结合不同层次的理论。我们揭示了兴奋酪氨酸中非常规和动态控制的“漫游”动力学，这些动力学超出了化学直觉，并与其他超快失活机制竞争。